A Perfect Storm: Ubiquity and Social Science

A keynote talk at a Ubicomp 2014 workshop. This talk looks at the opportunities for social science due to ubiquitous computing and offers some techniques for problem finding, problem formulation and problem reframing.

6.
Early Studies of Query Languages (1974)
❖ Query By Example showed great improvement over IQF in a user’s ability to
translate questions from English into formal query language: !
❖ IQF 4-24 hours training; QBE < 3 hours training!
❖ Ave. T/Query in IQF 5-12 min; QBE 1.6 min.!
❖ % correct IQF: 35%; QBE 67%!
❖ BUT: When given a series of problems and a DB description and asked to write
their own relevant query and translate into QBE, users could not do it.!
❖ Answered question (without being able to look at any actual data!).!
❖ Wrote (and translated) irrelevant queries.!
❖ Wrote “HAL+” queries.
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7.
Some Methods of Community Knowledge Generation and Sharing
(besides math models at one extreme and opinion at the other extreme)
❖ From General to Particular:
Story!
❖ From Particular to General:
Patterns and Pattern Language!
❖ Reframing: Context Generation
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9.
Abstraction: Random Drops Become a Lake
❖ Main tendency but with variation !
❖ Extreme outliers have qualitatively different
behavior!
❖ The behavior of the extreme outliers changes
the field; in particular, makes the probability
of other extreme outliers increase!
❖ Another example from The Power of Positive
Deviance: How Unlikely Innovators Solve the
World’s Toughest Problems. Childhood hunger
in Vietnam.!
❖ In this case, the positive feedback loop did
not exist without intervention. !
❖ Ubiquity could be used to help find such
“positive deviance”
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10.
Two Tables (Blue) supports stable community of 20-30 people every noon
What happens with One Table?
❖ H1: Community will stay at about 20-30 people (the
interesting in table tennis trumps facility).!
❖ H2: Community will diminish to about 15-20 people
(the facility will not support so many people).

12.
Problem Finding from Story
❖ Stories deal with the “edges” of human
experience!
❖ Stories thrive on conflict !
❖ Stories thrive on emotion!
❖ Follow the Anger back to source of
frustration: A problem to be solved.!
❖ In stories, typically it is the determination,
cleverness, or bravery of the hero that
saves the day.!
❖ However, they often have a special power
or gift: Make that a reality. !
❖ Or, “re-write” the story so that the
problem(s) can still be solved, but by
“ordinary” people.
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13.
Stories tend to focus on the “edges”
of human experience
(Note similarity to Patterns of
Behavior that Violate Expectations)
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16.
Problem Finding Examples:
❖ Pets do not always do what they should. Reinforcement works, but owners
are busy and away. S: Remote monitoring and delivery of reinforcement. !
❖ Home objects have instructions that are illegible. S: Mobile phone could
“read” what the device is and display legible instructions. !
❖ Plant signs are ambiguous. S: Photo sent to service which returns four
similar pictures with names and links. !
❖ New inventions promise wonders but lack convincing experiential
evidence. S: !
❖ Waiting turn for haircut is a pain. Plus, hard to describe how short you
want your hair to be cut. S: While waiting, iteratively choose haircut view
on based on your photo.
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17.
❖ Pets do not always do what they should. Reinforcement works, but owners
are busy and away. S: Remote monitoring and delivery of reinforcement.
❖ Planning the next !
“Catastrophe”
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18.
Home objects have instructions that are illegible. S: Mobile phone
could “read” what the device is and display legible instructions.
❖ Top view: Bose DVD player!
❖ Bottom view: Home thermostat!
❖ The “real” objects are just this!
difficult to read.!
Mobile device also allows a UX!
“intervention point” for updates,!
different languages, large print, etc
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21.
❖ New inventions promise wonders but lack convincing
experiential evidence. S: ??
❖ Grill cleaner, new skates,!
mosquito hood and jacket,!
rain barrel !
❖ What do these feel like?!
❖ How long do they last?!
❖ What are maintenance issues?!
❖ Will this still seem cool when !
I am not at 40,000 feet and have!
just had 3 martinis?!
❖ What if all these inventions were
instrumented BOTH for continuous
improvement AND so potential buyers could
see how they actually performed?
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24.
According to Judy Mod, founder of Social Executive Council
❖ Companies who produce and
sell focus most of their energy
on “beating the competition”
on price, performance,
features, etc.!
❖ For IT system decisions,
10-20% of lost sales prospects
are to competition.!
❖ 80-90% are lost to “no
decision”
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25.
Companies do “Market Research” but …
❖ Largely constrain the nature of the presumed problem up
front.!
❖ Study with ecologically invalid methods (e.g., “New Coke”).!
❖ Focus on beating the competition. !
❖ Focus on selling the product…but cannot see what it “looks
like” from the customer’s viewpoint.!
❖ “It’s a clown. It is smiling. It has big eyes. It has all the
features that our research shows are correlated with
cuteness. It has to be cute!”
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27.
Ubiquitous Computing Allows:
❖ Studying in situ both physically (in the
small and in the large) and “socially” !
❖ Caveat: Still subject to interpretation!
❖ Pattern: Reality Check!
❖ Which one is the “real” desk?
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28.
Pattern: Reality Check
❖ Often something easy to
measure is highly correlated
with what you really want to
measure.!
❖ You measure this “ersatz”
measure.!
❖ But, the correlation may change
over time. (e.g., programming
skill and speed).!
❖ Therefore, you need to
periodically do a reality check.
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30.
Where’s Jonathan?! Supposed to be here at 8:00; now 8:15!
❖ He doesn’t care about the project!!
❖ OR….Your appointment book
has the wrong time.!
❖ OR…Your watch is wrong.!
❖ OR…Jonathan comes from a
culture where 8:15 is not late.!
❖ OR…Jonathan was waylaid in
the hall by the CEO to talk about
the project. !
❖ OR…You are in the wrong room.
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32.
Social Pattern: “Who Speaks for Wolf?”!
❖A lot of effort and thought goes into
decision making and design.
❖Nonetheless, it is often the case that bad
decisions are made and bad designs
conceived and implemented primarily
because some critical and relevant
perspective has not been brought to bear.
❖ This is especially often true if the
relevant perspective is that of a
stakeholder in the outcome.
❖ Therefore, make sure that every relevant
stakeholder’s perspective is brought to
bear early.
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33.
Spatial Pattern: Context-Setting Entrance!
❖ Because people function in many different contexts
and come from many different backgrounds and
cultures, there are a wide variety of behaviors that
are considered “appropriate” in various
circumstances.
❖ Sometimes, we are expected to compete with each
other vigorously. Other times, we are expected to be
highly cooperative.
❖ When our own expectations are violated, we may
feel resentful, angry, or afraid. When we violate what
we later find to be the expectations of others, we may
feel embarrassed or resentful.
❖ We don’t want to be the only person at a party to
show up in a tux while everyone else is in blue jeans
--- or vice versa.
❖ Therefore, provide a context-setting entrance so that
people know what is appropriate.
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34.
Information Pattern: Clarification Graffiti
❖ Often people design formal
information systems without an
adequate understanding of what the
world is like to the end user.!
❖ When a user comes upon a puzzling
situation, they sometimes find a
solution. !
❖ Often, when this happens, the user
wants to share what they learned
with others.!
❖ When possible, this leads to informal
annotations that help clarify what is
really meant for other users.
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35.
Temporal Pattern: Small Successes Early
❖ Some problems require large teams of relative
strangers to work together cooperatively in order
to solve the overall problem.
❖ Yet, people generally take time to learn to trust
one another as well as to learn another's
strengths and weaknesses and preferred styles.
❖ Plunging a large group of strangers immediately
into a complex task often results in non-productive
jockeying for position, failure,
blaming, finger-pointing, etc.
❖ Therefore, insure that the team or community
first undertakes a task that is likely to bring some
small success before engaging in a complex
effort.
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36.
Major Challenges: Scientific and Ethical
❖ Technology keeps changing; people
keep learning; tasks and goals and
contexts keep changing and
expanding —> How can we cumulate
science?!
❖ Query Study!
❖ www.ibm.com!
❖ We may be able to accurately
(statistically) predict “bad behavior”
before it occurs.!
❖ Who decides when, how, and
whether to intervene?!
❖ Minority Report; The Circle
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38.
Three different Disciplines are Converging:
Science
Invention
Operations
Hypothesis: The “perfect storm” allows
on-going measurement, refinement,
improvement, reframing, reinvention,
and scientific discovery all at the same
time from using the same data and using
various combinations of the same methods.
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39.
Science
❖ “Triple Blind” experiments:
people do not even know they
are in a study. Ethical? !
❖ Contingent Experiments:
Rather than “pre-plan” the
entire experiment, conditions
evolve and multiply as
evidence accumulates. !
❖ In Situ experiments: As more of
the real world conditions can
be monitored and dealt with,
less need to perform in lab.
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Invention
❖ More scientific studies of the
invention processes will
snowball number and breadth
of inventions. !
❖ “Brute Force” exploration will
happen more quickly. (e.g.,
light bulb, lead storage battery,
scrabble). !
❖ The instrumentation of reality
will lead to finding a great
number of problems to be
solved.
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42.
Key to Making this All Happen is You and Your Approach
❖ Using your knowledge, skill,
and a variety of sophisticated
techniques while inside…!
❖ Still being the inquisitive child.!
❖ To boldly go ….
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